Abstract
This study aims to extract the high-level features of driving fatigue using electroencephalography (EEG). The commonly used feature selection method is power spectral density (PSD) of five frequency bands, i.e., Alpha, Beta, Gamma, Delta and Theta band. This study proposes a new approach combined with ensemble empirical mode decomposition (EEMD) and PSD. EEMD provides several Internal Mode Function (IMF) components that can be used to extract PSD features. Multiple machine learning approaches, i.e., k-Nearest Neighbor (KNN), support vector machine (SVM), and hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM), were used to evaluate the two feature selection methods. The results show that the accuracy based on EEMD’s PSD is obviously superior to feature extraction of frequency band’s energy spectrum. By comparing the accuracies, we came to the conclusion that the new feature selection method using the PSO-H-ELM classifier performed better with the highest average accuracy of 94.58%.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under grant 62071161, 61871427 and Key R&D projects of Shandong Province under grant 2019JZZY021005.
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Luo, Z., Zheng, Y., Ma, Y., She, Q., Sun, M., Shen, T. (2022). A New Feature Selection Method for Driving Fatigue Detection Using EEG Signals. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_59
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DOI: https://doi.org/10.1007/978-981-16-6554-7_59
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